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Do It Yourself: Social Network Analysis Professor Dan Brass (J. Henning Hilliard Professor of Innovation Management at University of Kentucky) will describe how to do social network analysis in organizations. A social network is a set of actors (individuals, groups, organizations) and the relationships that connect them. Professor Brass will describe how to collect social network data, review the typically used network concepts and measures, and explain how to analyze the data. Concepts include centrality, density, cliques, structural equivalence, structural holes, centralization, and others. Information about software packages is also included. Prof. Brass will also review many of the research findings using social network analysis in organizations.

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Social Network Perspective Actors are embedded within a web (network) of interrelationships with other actors. Network: set of nodes (actors) and ties representing some relationship, or lack of relationship, between the nodes.

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Social Network Perspective Focus is on relationships, and the structure of these relationships, rather than the attributes of the actors. Networks provide the opportunities and constraints – patterned relationships among multiple actors affect behaviors, attitudes, cognitions, etc.

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Social Capital The idea that one’s social contacts convey benefits that create opportunities for competitive success for individuals and for the groups in which they are members. (Bourdieu, 1972; Burt, 1992; Coleman, 1988; Fukuyama, 1995; Gabby, 1997; Putnam, 1995) “The sum of the actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit.” (Nahapiet & Ghoshal, 2000: 243)

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Development of the Field # of social network papers in sociology by year; Borgatti & Foster, 2003

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Centrality Degree: number of ties Closeness: number of links it takes to reach everyone else in the network Betweenness: extent to which actor falls between any other two actors in the network (structural holes)

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Closeness Centrality Number of links it takes to reach every other actor in the network. Measure for the Kevin Bacon game. Measure for the “small world” phenomenon: “6 degrees of separation”

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How to Collect Social Network Data Collect relational as opposed to attribute data. Ask people to: List names - open Circle names on a roster – bounded Questions can be about any relationship: Who do you consider to be a friend? Who do you go to for advice? Who do you talk to frequently? Between any set of actors Individual people Groups Organizations

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How to Collect Social Network Data Ego networks: centered around a particular actorl. Includes the “ego” and direct tie “alters,” and ties among the alters. One actor’s network. Whole networks: attempt to get data from all members of a bounded network.

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Age: _______________years How long have you worked for UHS? ____________years How long have you worked in your present job? __________years Please check those that apply: High school diplomaBachelor’sM.D.Physician’s Assistant Associate’sMaster’sR.N.Nurse Practitioner Other (please specify) ____________________ Please check the shift during which you normally work: DayNightSwingRotate shifts For each person below, please check the boxes that apply (check as many as are applicable). Consider a friend Consider an acquaintance Go to for advice Go to for support BUSINESS OFFICE Joslyn Armstrong Staci-Jo Bruce Myrna Covington Donna Decker Donna Gibboney Lorraina Hazel Debra Hoover Kim Johnson Tom Lawton Connie Mann Joe Reilly Pat Robinson Carolyn Schenk Has the following amount of influence in UHS (please rate on the scale below) Usually communicate with (please rate on the scale below ) Are required to interact with because of the nature of your work Prefer to avoid Seldom (less than once a week) Often (many times a day) Very little influence A great deal of influence 1 2 3 4 5

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How to Collect Social Network Data We can collect valued data as well as binary data. Binary – yes or no, 1 or 0 Valued – example: on a scale from 1-7 We can also collect data about affiliations. Example: Archival data on boards of directors.

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How to Collect Social Network Data We can also collect attribute data. Enter it as a one column vector; transform it to similarity/dissimilarity matrix.

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How to Collect Social Network Data Actors are not very good about remembering specific interactions. Bernard et al. 1984 But they are good about remembering recurrent, repeated interactions or on-going relationships. Freeman et al. 1987

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How to Handle Social Network Data Because the data are relational, we enter them in a matrix. Actor by actor square “adjacency” matrix (one mode) Actor by affiliation rectangular “affiliation” matrix (two mode). UCINet has several ways to enter data, spreadsheet may be most simple. Each cell in the matrix indicates if the actors are related (1,0) or the extent of the relationship (1-7). Data are “directional” from rows to columns (i to j). (Down left side, across columns) Cells are also referred to by row and column (cell 3,4 is row 3, column 4)

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How to Handle Social Network Data Directional data provides measures such as: in-degree: number of links coming in to the actor out-degree: number of links going out from the actor Directional data can be symmetrized. Valued data can be converted to binary.

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How to Analyze Social Network Data Make decisions about symmetry (binary and valued). Can symmetrize on higher value, lower value or average value. Advice network is directional – do not symmetrize. Communication network is non-directional – symmetrize. Others – check reciprocation rate. Follow up to resolve discrepancies.

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How to Analyze Social Network Data Save matrix in UCINet – give it a name. All UCINet procedures ask for matrix input. Just input matrix and it will print out values for the measure. You can enter values (e.g., centrality) into SPSS or SAS programs and correlate or regress like normal (e.g., centrality with power scores)

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How to Analyze Social Network Data Some network measures identify an actor’s position in the network. Although these measures are assigned to individual actors, they are a result of the relationships within the network. Example: centrality. We can also look at measures that describe the entire network. Example: density – actual number of ties that exist divided by the total number of possible ties (n(n-1). We can also use network measures to identify groups within the network. Example: cliques – a subset of nodes in which every possible pair of nodes is directly connected and the clique is not contained in any other clique. Cliques can be of any size.

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How to Analyze Social Network Data If you do matrix by matrix correlation or regression, you must use UCINet procedure called QAP (Quadratic Assignment Procedure) because observations are not independent. QAP generates 1000-2000 random permutations of the independent matrix, then computes the correlations with the dependent matrix. The procedure computes the proportion of coefficients generated from the random permutations that are as extreme as the coefficient between your two matrices. Enter two or more matrices and it will give you correlation or regression results and significance levels.

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Actor Similarity (Homophily) Similarity matrix – cell indicates if two actors are similar on some characteristic (binary or valued). Enter vector (one column) of attribute data and input into UCINet “similarity” procedure. Result is actor by actor square matrix. You can then QAP correlate similarity matrix with interaction matrix.

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Structural Equivalence Actors are structurally equivalent to the extent that they have similar patterns of interaction with other actors, even if they are not connected to each other. (Concor) Regular Equivalence: actors have same patterns of relationships even if connections are not to the same others. (ExcatRege)

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Getting Ahead Brass, 1984, 1985 - central (closeness & betweenness) actors in departments promoted during following three years. Boxman, De Graaf, & Flap, 1991 - 1359 Dutch managers, external work contacts and memberships related to income attainment and level of position (number of subordinates) controlling for human capital (education and experience). Return on human capital decreases as social capital increases. No difference for men and women. Burt, 1992 - White males who were promoted quickly had structural holes in their personal networks; women and new hires did not benefit from structural holes.

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Negative Asymmetry Negative events and relationships may have more impact than positive events and relationships. Negative events are rare. Thus, we pay more attention to them, view them as more diagnostic (“true nature shows”).